The Real Python Podcast

How does Python manage memory behind the scenes? What are the common ways to lay out a Python project? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article titled “First Steps into Memory Management in Python.” With recent updates to the garbage collector and the ongoing work toward removing the Global Interpreter Lock (GIL), the article covers the fundamentals of how Python allocates, tracks, and frees memory.

We also share other articles and projects from the Python community, including recent releases, why Parquet dominates modern data engineering, choosing the right Python project layout, developing the most cursed + operator in all of Python, finding which dependencies block a Django project upgrade, and a Django performance benchmark profiler.

Spotlight: Codex for Python Developers: Hands-On Agentic Coding Course

Most Python developers use AI as fancy autocomplete. This 2-day live course teaches you to build entire projects with Codex, an AI agent that works inside your codebase.

Get Your Cheat Sheet: Click here to download the free cheat sheet for choosing your project layout and keep the flat vs. src/ decision and the entry-point wiring within reach on your next project.

Topics:

• 00:00:00 – Introduction
• 00:01:56 – Python 3.16.0 Alpha 1 Is Now Available
• 00:02:24 – Django Security Releases Issued: 6.1.2, 6.0.9, and 5.2.18
• 00:02:46 – Release of Polars 2.0
• 00:03:32 – PyPy v8.0.1 Release
• 00:03:54 – Checking Email Reputation, and Allowing Outlook Again
• 00:04:48 – PyScript is in maintenance mode
• 00:05:55 – Codex for Python Developers: Hands-On Agentic Coding Course
• 00:06:21 – MLOps North: November 5th & 6th
• 00:06:46 – Why Parquet Dominates Modern Data Engineering
• 00:16:04 – First Steps Into Memory Management in Python
• 00:23:21 – Spotlight: Codex for Python Developers
• 00:25:05 – Python Project Structure: How to Choose the Right Layout
• 00:35:17 – Pendulum and the Most Cursed + Operator in All of Python
• 00:38:50 – django-benchmark: Django Performance Benchmark Profiler
• 00:41:53 – django-upgrade-report: Which Dependencies Block an Upgrade to Your Django Project?
• 00:44:33 – Thanks and goodbye

News:

• Python 3.16.0 Alpha 1 Is Now Available
• Django Security Releases Issued: 6.1.2, 6.0.9, and 5.2.18
• Release of Polars 2.0
• PyPy v8.0.1 Release
• Checking Email Reputation, and Allowing Outlook Again – Two years ago, PyPI prohibited Outlook email domains in the registration of new accounts due to a large malware campaign. With an update to how email reputation is calculated, addresses from outlook.com and hotmail.com can once more be used.
• PyScript is in maintenance mode. Here’s what’s left for Python in the browser - DEV Community
• Codex for Python Developers: Hands-On Agentic Coding Course – Real Python
• MLOps North: Building (with) Agents Tickets, Thursday, November 5-Friday, November 6 • 8:30 AM-3 PM | Eventbrite

Show Links:

• Why Parquet Dominates Modern Data Engineering – How Parquet’s columnar layout, encodings, zstd compression, and row group statistics make it smaller and faster to read than CSV, measured on NYC taxi trips data with Polars, DuckDB, and PyArrow.
• First Steps Into Memory Management in Python – A quick introduction to how Python manages memory under the hood and how that might affect your code.
• Python Project Structure: How to Choose the Right Layout – Learn how to structure Python projects: pick between flat and src/ layouts, organize your code, and apply the patterns to CLI, web, and GUI apps.
• Pendulum and the Most Cursed + Operator in All of Python – Pendulum, a popular Python datetime library, has a + operator that inspects the call stack to decide which answer to give you. The post explains how it ended up there, and why the design behind it, a “drop-in” subclass of datetime, still matters for your code now that the hack itself has been patched.

Projects:

• django-benchmark: Django Performance Benchmark Profiler
• django-upgrade-report: Which Dependencies Block an Upgrade to Your Django Project?

Additional Links:

• PEP 848 – Generational Incremental Garbage Collection | peps.python.org
• Reverting the incremental GC in Python 3.14 and 3.15 - Core Development - Discussions on Python.org
• Garbage Collection: Python 3.15.2 documentation
• Memory Management in Python – Real Python

Level up your Python skills with our expert-led courses:

• How Python Manages Memory
• How to Set Up a Django Project
• Using Astropy for Astronomy With Python

Support the podcast & join our community of Pythonistas

How is the Python ecosystem adapting to the modern landscape of software security and supply chain defense? This week on the show, we sit down with Seth Larson, the Python Software Foundation’s Security Developer in Residence.

Seth shares his background in open source and how he got involved in security engineering. We discuss the Python Security Response Team and how a recent PEP formalized its processes for reporting and triage.

Seth breaks down how large language models are affecting vulnerability discovery and increasing the volume to triage. We also cover the security advantages of trusted publishing on PyPI, how Software Bill of Materials (SBOMs) under PEP 770 help track dependencies, and some advice on improving your CI/CD security.

Course Spotlight: Testing Your Code With Python’s unittest

Learn how to use Python’s unittest framework to write unit tests for your code, including test cases, fixtures, and test suites.

Free Bonus: Python Security Checklist

Get a Python Security Checklist (PDF) and keep secrets, passwords, encryption, HTTPS, input validation, and dependency audits on one printable page:

Topics:

• 00:00:00 – Security Note: Python certifi package and PHP updates
• 00:00:37 – Introduction
• 00:02:22 – Pikmin fans unite!
• 00:02:51 – How did you get involved in security research?
• 00:07:07 – Security Research is unique and the wider community
• 00:13:35 – The influx of LLMs finding vulnerablities
• 00:17:18 – The Python Security Response Team and PEP 811
• 00:25:21 – Video Course Spotlight
• 00:26:44 – Is the flood of vulnerabilities unlimited?
• 00:29:15 – Security Track at PyCon US 2026
• 00:31:03 – Does a security engineer need humility?
• 00:33:25 – PEP770 Improving measurability of Python packages with Software Bill-of-Materials
• 00:38:55 – Communication across other languages and package repositories
• 00:41:41 – What’s it been like writing PEPs?
• 00:44:32 – Defining the term Trusted Publisher
• 00:47:27 – Differences between public and private vulnerabilities
• 00:51:19 – Are you using LLMs in your work as a security researcher?
• 00:52:56 – The fear/hype cycle of vulnerabilites and triaging reports
• 00:58:35 – Do you have security advice for developers?
• 01:03:12 – What are you excited about in the world of Python?
• 01:06:14 – What do you want to learn next?
• 01:08:08 – How can people follow your work online?
• 01:09:58 – Thanks and goodbye

Show Links:

• Seth Larson - Website
• Removal of emSign root cerificate - Advisory - certifi/python-certifi
• Supply Chain Security for the Age of AI-Speed Exploits – Real Python
• urllib3 - PyPI
• PSF Developers in Residence - Python.org
• Keynote: Security Work isn’t Special - Seth Larson - OpenSSF Community Day
• Phantom Dependencies: is your requirements.txt haunted? - Seth Michael Larson - YouTube
• PSF - Update from our Security Engineers - YouTube
• Python Security: What we’ve done, now AI & Glasswing are changing everything (Sponsor: Alpha-Omega) - YouTube
• Learning from the not-so-secret Python security “cabal” - Seth Michael Larson - YouTube
• PEP 811 – Defining Python Security Response Team membership and responsibilities
• PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials
• PEP 770 Software Bill‑of‑Materials (SBOM) data from PyPI, Fedora, and Red Hat — Seth Larson
• Trusted Publisher - Getting Started - PyPI Docs
• zizmor: Static analysis for GitHub Actions (and more)
• Pikipedia, the Pikmin wiki
• Rust for CPython (Python Language Summit 2026) | Python Insider
• Python Software Foundation News
• Python Insider
• Seth Larson - Website

Level up your Python skills with our expert-led courses:

• Python Continuous Integration and Deployment Using GitHub Actions
• Testing Your Code With Python's unittest
• How to Get Started With Ollama

Support the podcast & join our community of Pythonistas

What are common mistakes that Python beginners make, and how do you avoid them? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We dig into an article about common pitfalls and mistakes new Python developers make. Some of the mistakes are due to Python doing exactly what you asked it to do. We also talk about Real Python resources for getting started with the language and building your development environment.

We also share other articles and projects from the Python community, including recent releases, a collection of new PEPs, and the results of two recent Python elections. We cover creating temporary files in Python, decoupling pure logic from Django’s ORM, a Python functions reference, a lightning-fast logger, and an online development environment for creating games.

Course Spotlight: How to Use Python: Taking Your First Steps

Take your first steps with Python. Install it, run your first program, and work with variables, data types, loops, functions, and errors.

Live Course Spotlight: Python for Beginners: Code With Confidence

Learn the fundamentals of Python step-by-step in a friendly, interactive cohort. Build confidence writing code and understand the “why” behind Python’s core concepts.

Free Kit: Get the Python Beginner Starter Kit with a cheat sheet, a sample book chapter, a course, and a quiz. You only need a free Real Python account.

Topics:

• 00:00:00 – Introduction
• 00:02:05 – After a delay - Python 3.15.0 (final) is here!
• 00:02:41 – Python 3.10.22, 3.11.17, 3.12.15, 3.13.16 and 3.14.8 are now available!
• 00:03:30 – PEP 823 – None-aware access operators
• 00:04:40 – PEP 845: Leading-Dot Value Patterns
• 00:05:04 – PEP 846: Docstrings for Type Aliases
• 00:05:21 – PEP 848: Generational Incremental Garbage Collection
• 00:06:12 – 2026 PSF Board Election Results
• 00:06:31 – 2026 Python Packaging Council Election Results!
• 00:07:23 – 7 Python Mistakes Beginners Make
• 00:18:03 – An Effective Python Development Environment
• 00:21:37 – Creating Temporary Files in Python
• 00:24:30 – Video Course Spotlight
• 00:26:02 – 5 Design Patterns Used in My New Habit Tracker App
• 00:33:43 – Functions: A Complete Reference
• 00:36:50 – PYXEL STUDIO
• 00:39:38 – pyfastlogging: A Lightning Fast Logger, Written in Rust
• 00:41:54 – How Libraries Run Rust Inside Python (with PyO3)
• 00:44:43 – Thanks and goodbye

News:

• Python 3.15.0 (final) is here!
• Python 3.10.22, 3.11.17, 3.12.15, 3.13.16 and 3.14.8 are now available!
• PEP 823 – None-aware access operators
• PEP 824 – None-coalescing operators
• PEP 845: Leading-Dot Value Patterns (Draft)
• PEP 846: Docstrings for Type Aliases (Draft)
• PEP 848: Generational Incremental Garbage Collection (Draft)
• 2026 PSF Board Election Results
• 2026 Python Packaging Council Election Results!

Show Links:

• 7 Python Mistakes Beginners Make – Some mistakes are because Python did exactly what you asked it to do. This article covers seven of them. For each one, you get the hidden cause, plus the first thing worth checking.
• An Effective Python Development Environment – Choose a Python development environment that helps you get coding. Find tutorials and courses on editors, uv, virtual environments, and useful tools.
• Creating Temporary Files in Python – How to create temporary files and directories in Python using the tempfile module’s NamedTemporaryFile and TemporaryDirectory.
• 5 Design Patterns Used in My New Habit Tracker App – Bob has built a Django + HTMX-based habit tracker. This article covers five small Python and design patterns used in his code.
• Functions: A Complete Reference – This article is a complete reference for all things related to functions in Python, including structure, signatures, *args, **kwargs, using functions as objects, and more.

Projects:

• PYXEL STUDIO
• pyfastlogging: A Lightning Fast Logger, Written in Rust

Additional Links:

• Install packages in a virtual environment using pip and venv - Python Packaging User Guide
• Python Virtual Environments: A Primer – Real Python
• Require a Virtualenv for pip Installs - pydevtools
• One namespace to namespace them all (Python Language Summit 2026) - Python Insider
• commitgraph - Commit to what matters. Every day.
• Getting Started with Rust as Python Devs - Talk Python To Me Podcast
• How Libraries Run Rust Inside Python (with PyO3) - Bob Belderbos

Level up your Python skills with our expert-led courses:

• Python Basics: Setting Up Python
• Python Basics: Code Your First Python Program
• How to Use Python: Taking Your First Steps

Support the podcast & join our community of Pythonistas

How do you assemble the core pieces of a coding agent? How are developers defining projects through conversation and introspection? This week on the show, Geir Arne Hjelle returns to talk about his recent project to demonstrate how coding agents work under the hood.

Geir Arne was my first guest on this program and has returned often over the years. He’s been exploring how coding agents work and has built a demonstration tool to explain them.

We also discuss how he is using LLM tools in his work. We talk about moving beyond Spec-Driven Development to using tools that help you build architecture documents through thorough questioning sessions.

Course Spotlight: Modern Python Linting With Ruff

Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular.

Topics:

• 00:00:00 – Introduction
• 00:01:52 – Codemoo - Demonstrating how coding agents work under the hood
• 00:06:39 – Exploring Spec-Driven Development
• 00:09:38 – Breaking into the levels of a harness
• 00:17:08 – Tool calling
• 00:24:20 – Being a newbie again
• 00:29:00 – Designing the progression
• 00:35:29 – Video Course Spotlight
• 00:37:01 – What was difficult about the process?
• 00:41:09 – What tools are you using currently?
• 00:44:10 – Moving from OpenSpec toward grill-me
• 00:50:43 – Deciding on which LLM
• 00:53:49 – garuff: Opinionated Python linter rules for coding agents
• 00:58:00 – Offsetting atrophy with code challenges
• 01:01:56 – What are you excited about in the world of Python?
• 01:04:01 – What do you want to learn next?
• 01:05:55 – How can people follow your work online?
• 01:06:23 – Thanks and goodbye

Show Links:

• codemoo: Demonstration of how coding agents work under the hood - GitHub
• codemoo - PyPI
• configaroo: Bouncy configuration handling
• koalaty: Evaluate and compare models in agent harnesses
• Episode #277: Moving Towards Spec-Driven Development
• OpenSpec: Spec-driven development (SDD) for AI coding assistants.
• Build your own Claude Code - CodeCrafters
• claude-howto: A visual, example-driven guide to Claude Code
• Matt Pocock: Become a Real AI Hero
• The /grill-me Skill
• mattpocock/skills: Skills for Real Engineers. Straight from my .agents directory.
• garuff: Personal and opinionated Python linter rules aimed at coding agents
• garuff - PyPI
• Advent of Code 2025
• Advent of Code: Solving Your Puzzles With Python – Real Python
• Everybody Codes
• dailyprog.club: A daily programming puzzle app. - Codeberg.org
• FlipFlop
• Python 3.15 Preview: Lazy Imports – Real Python
• Milky Oolong - A.C. Perch’s Tehandel
• Gongfu tea - Wikipedia
• How to Brew Tea Properly
• gahjelle (Geir Arne Hjelle) / Repositories

Level up your Python skills with our expert-led courses:

• Modern Python Linting With Ruff
• Tips for Using the AI Coding Editor Cursor
• Using the Claude API in Python

Support the podcast & join our community of Pythonistas

Python 3.15 is (almost*) here! Christopher Trudeau and Bartosz Zaczyński return to discuss the new version. Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.15: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.15” covers the topics from the article and shows the new features in action.

Christopher and Bartosz dug into the release to create code examples of the new features for the tutorial and course. We cover the new built-in types, unpacking in comprehensions, and developer experience enhancements. We also look into the performance improvements, including lazy imports, the upgraded JIT compiler, and a new sampling profiler.

We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.15.

Course Spotlight: What’s New in Python 3.15

Explore the new features in Python 3.15, including lazy imports, frozendict, sentinel values, a sampling profiler, and a faster JIT.

Topics:

• 00:00:00 – Introduction
• 00:00:02 – * NOTE: Python 3.15 release delayed!
• 00:03:35 – Real Python 3.15 Preview Articles and Video Course
• 00:04:40 – Sentinel Values
• 00:08:55 – Lazy Imports
• 00:14:48 – Unpacking in Comprehensions
• 00:17:12 – Mathematical functions
• 00:20:07 – Sampling Profiler
• 00:27:43 – Video Course Spotlight
• 00:29:18 – frozendict
• 00:33:12 – The speed round of features
• 00:36:04 – Continued work on free-threaded Python and upgrading the JIT
• 00:39:34 – Upgrading to 3.15
• 00:44:35 – Thanks and goodbye

Show Links:

• * Python 3.15: following tradition, let’s have a surprise rc3! - Core Development - Discussions on Python.org
• Python 3.15: Cool New Features for You to Try – Real Python
• Python 3.15 Preview: Sentinel Values – Real Python
• Python 3.15 Preview: Lazy Imports – Real Python
• Unpacking in Comprehensions -What’s new in Python 3.15 — Python 3.15 documentation
• math — Mathematical functions — Python 3.15 documentation
• Python 3.15 Preview: Sampling Profiler
• PEP 799 – A dedicated profiling package for organizing Python profiling tools
• Python 3.15 Preview: frozendict
• PEP 814 – Add frozendict built-in type
• More color - What’s new in Python 3.15
• Improved error messages - What’s new in Python 3.15
• Python 3.15 Preview: UTF-8 by Default
• PEP 803: Stable ABI for free-threaded builds - What’s new in Python 3.15
• Python 3.15 Preview: Upgraded JIT Compiler

Additional Links:

• PEP 810 – Explicit lazy imports
• Implement native PEP 0810 lazy loading by hebaalazzeh - google-cloud-python
• PEP 798 – Unpacking in Comprehensions
• PEP 448 – Additional Unpacking Generalizations
• Tachyon: Python 3.15’s sampling profiler is faster…- Pablo Galindo Salgado, Laszlo Kiss Kollar - YouTube
• The amazing and varied life of Tachyon particles in Star Trek TNG - YouTube
• Episode #128: Using a Memory Profiler in Python & What It Can Teach You
• Episode #172: Measuring Multiple Facets of Python Performance With Scalene
• Upgraded experimental JIT compiler - What’s new in Python 3.15
• What’s new in Python 3.15 — Python 3.15 documentation

Level up your Python skills with our expert-led courses:

• Understanding Python List Comprehensions
• What's New in Python 3.14
• What's New in Python 3.15

Support the podcast & join our community of Pythonistas

How should you manage AI contributions to an open-source project? How do you measure the impact of using AI tools for development, and what generative features do end users want from a content management system? This week on the show, we speak with Thibaud Colas and Meagen Voss from Wagtail about the complex considerations software organizations are currently facing.

Meagen is the Wagtail community manager, and Thibaud is a member of the core team. We discuss the impact of AI on a popular open-source project. We reference multiple articles they’ve written about managing these challenges.

We dig into wrangling AI-assisted contributions to the project. They share how they research options for open-weight AI models and inference providers. We also discuss building AI features into the Wagtail CMS. Finally, we touch on the recent release of Wagtail 8.0.

Course Spotlight: Using the Claude API in Python

Learn how to use the Claude API in Python to send prompts, control responses with system instructions, and get structured output.

Topics:

• 00:00:00 – Introduction
• 00:01:49 – What is Wagtail?
• 00:08:35 – Flood of AI submissions to open-source projects
• 00:11:38 – Guidance for AI-led contributions
• 00:20:43 – Security submissions
• 00:21:56 – Ethical decisions about internal use of AI
• 00:26:31 – Resurgence of web sites
• 00:27:41 – Measuring benefits and performance
• 00:32:49 – Models understanding your framework
• 00:38:05 – Video Course Spotlight
• 00:39:42 – Adding AI features to Wagtail for creators
• 00:48:47 – Balancing the use of AI internally and the cost of inference
• 00:55:29 – Advice for other open-source projects
• 01:00:00 – Wagtail 8.0
• 01:02:10 – What are you excited about in the world of Python?
• 01:04:10 – What do you want to learn next?
• 01:05:56 – How can people follow your work online?
• 01:06:43 – Thanks and goodbye

Show Links:

• Wagtail CMS - Django Content Management System
• Wagtail Space 2026 - Wagtail CMS
• Showcase - Wagtail CMS
• Guidance for AI-led contributions - Wagtail CMS
• What I learned from two days of hanging out with AI experts - Wagtail CMS
• AI saved me 10 minutes on a blog post: Was it worth it? - Wagtail CMS
• What AI tools get right and wrong with Wagtail - Wagtail CMS
• Comparing open weight AI models and providers - Wagtail CMS
• AI in the CMS: steering the ecosystem - Wagtail CMS
• The carbon footprint of Wagtail AI - Wagtail CMS
• Open source AI we use to work on Wagtail - Wagtail CMS
• Results of the 2026 Wagtail DX with AI survey - Wagtail CMS
• open-code-review: Secure, fast, efficient, battle-tested - hybrid architecture code review tool
• CMS with AI, not AI CMS: Wagtail 8.0’s new API
• Roadmap - Wagtail CMS
• Executive Director Search Extended to September 22 - Django Weblog
• git-worktree Documentation
• Wagtail newsletter
• Meagen Voss (@vossisboss@fosstodon.org) - Fosstodon
• Thibaud Colas (@thibaudcolas@fosstodon.org) - Fosstodon

Level up your Python skills with our expert-led courses:

• How to Set Up a Django Project
• Getting Started With Google Gemini CLI
• Using the Claude API in Python

Support the podcast & join our community of Pythonistas

What are the latest trends uncovered in the 2026 Django Developers Survey? How are Django users employing LLMs in their development process? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We discuss a summary by Will Vincent about this year’s Django Developers Survey. The survey draws on responses from nearly 3,500 developers across more than 40 countries. It provides a look at how developers are adapting to AI workflows and which tools they’re embracing.

We dig into a post from Brett Cannon about reproducible builds in Python. It covers the importance of creating an independently verifiable, repeatable build of CPython and other associated tools. The post explains why Python isn’t there yet and outlines the direction of the work still needed.

We also share other articles and projects from the Python community, including recent releases, upcoming PSF elections, Polars vs SQL differences nobody is talking about, creating Django unsubscribe links without a Login, a library to make measurement units easier through Pydantic, and a tool to convert if-else code to match statements.

This episode is sponsored by Six Feet Up.

Video Course Spotlight: How to Get Started With Ollama

Learn how to install Ollama, pull local models, and connect them to your Python code using the chat and text generation interfaces.

Topics:

• 00:00:00 – Introduction
• 00:02:46 – Python 3.15.0 candidate 2 is here!
• 00:03:10 – Django 6.1.1 release notes
• 00:03:20 – Wagtail 8.0 release notes
• 00:04:27 – RISC-V Is Now Officially Supported by CPython
• 00:05:19 – PEP 843: Export Statement for DRY Re-Exports
• 00:06:27 – 2026 PSF Board Election Interviews
• 00:07:07 – Up and Running with Rust Course
• 00:07:56 – Guido Comment on PEP 805: Safe Parallel Python
• 00:09:56 – The Polars vs SQL Differences Nobody Is Talking About
• 00:16:55 – Sponsor: Six Feet Up
• 00:17:50 – Unsubscribe Links Without a Login: Django Signing
• 00:20:56 – What’s Missing to Have Reproducible Builds on PyPI
• 00:27:09 – Video Course Spotlight
• 00:28:44 – The Python Community’s Institutional Response to the Astral Acquisition Has Begun
• 00:29:57 – The State of Django 2026: Boring Is So Back
• 00:40:13 – matchify: Converts if-else Code to match Statements
• 00:43:14 – pydantic-pint: Pydantic Pint Quantities
• 00:45:16 – Thanks and goodbye - Submit topics to PyCoders Link

News:

• Python 3.15.0 candidate 2 is here! - Python Insider
• Django 6.1.1 release notes - Django documentation
• CMS With AI, Not AI CMS: Wagtail 8.0’s New API – Wagtail 8 includes a new API built on Django Ninja and Pydantic, to automate common admin tasks with and without agents. 50+ operations derived from projects’ existing Python/Django code, mimicking the admin panel but via endpoints and an official CLI.
• Wagtail 8.0 release notes — Wagtail Documentation 8.0 documentation
• RISC-V Is Now Officially Supported by CPython
• PEP 843: Export Statement for DRY Re-Exports (Draft)
• PEP 842: Module Exports (Withdrawn)
• 2026 PSF Board Election Interviews – This is a collection of interviews of the various candidates running for the Python Software Foundation Board. Many of the posts also include links to AMA sessions.
• Python Software Foundation News: The 2026 PSF Board Election is Open!
• Python Software Foundation News: Inaugural Python Packaging Council Election: Voting is now open!
• Up and Running with Rust Course
• Guido Comment on PEP 805: Safe Parallel Python

Show Links:

• The Polars vs SQL Differences Nobody Is Talking About – Some problems can be attacked with either SQL or Polars, but subtle differences in how the two mechanisms work can run you into trouble. Learn more about these potential gotchas.
• Unsubscribe Links Without a Login: Django Signing – Django has a signing module that makes it easy to build an unsubscribe link that works with no login and no session: token = signing.dumps(recipient.pk, salt=UNSUBSCRIBE_SALT). The token itself is the credential, and it ships with Django out of the box.
• What’s Missing to Have Reproducible Builds on PyPI – A reproducible build is a way of creating an independently verifiable, repeatable build of CPython and other associated tools. This post explains why Python isn’t there yet and why it is important.
• The Python Community’s Institutional Response to the Astral Acquisition Has Begun – Brett Cannon posted on discuss.python.org (March 23): a PEP is coming, the python/prebuilt-cpython repo already exists, and the PSF has been building an official prebuilt relocatable CPython distribution since October 2025. Covers what’s actually being built, what it means for uv/ruff/python-build-standalone, and why the Astral upstream patches and PSF alternative aren’t in conflict.
• The State of Django 2026: Boring Is So Back – A summary of this year’s State of Django report which draws on responses from nearly 3,500 developers across more than 40 countries: from students in their first year to veterans with decades of experience.

Projects:

• matchify: Converts if-else Code to match Statements
• pydantic-pint: Pydantic Pint Quantities

Additional Links:

• Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
• Mastering DuckDB when you’re used to pandas or Polars
• Episode #297: Improving Python Through PEPs and Protocols
• PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials
• Phantom Dependencies: is your requirements.txt haunted? - Seth Michael Larson - YouTube
• prebuilt-cpython - GitHub
• > htmx - high power tools for html
• Pint: makes units easy — pint 0.25.3rc1.dev1+g5e79411e1 documentation
• Exploring Astrophysics in Python With pandas and Matplotlib – Real Python
• PyCoder’s Weekly - Submit a Link

Level up your Python skills with our expert-led courses:

• Exploring Astrophysics in Python With pandas and Matplotlib
• Connecting LLMs to Your Data With Python MCP Servers
• How to Get Started With Ollama

Support the podcast & join our community of Pythonistas

How do you plan for the performance of your Python applications? What does a performance budget entail, and where should you spend your resources? This week on the show, we speak with Den Odell about his new book “Fast by Default: Practical Performance Engineering.”

Den has 25 years of experience in building web systems for companies with hundreds of millions of users. Working on public-facing tools has honed his skills in understanding where performance is vital and in guiding decisions based on data from actual users.

Den shares details of his performance framework that works across any platform or stack. We discuss the need for budgeting performance during the planning phase, measuring performance through consistent profiling, and keeping systems fast even as your codebase and user base grow.

Course Spotlight: Profiling Performance in Python

Learn to profile Python programs with built-in and popular third-party tools, and turn performance insights into faster code.

Topics:

• 00:00:00 – Introduction
• 00:01:39 – Modern systems run across multiple languages
• 00:03:52 – What lead you toward performance engineering?
• 00:12:54 – Frameworks and balancing performance against developer experience
• 00:17:45 – Measuring performance and profiling
• 00:24:28 – What is Fast by Default?
• 00:36:34 – Video Course Spotlight
• 00:38:16 – Breaking into the methodology
• 00:42:10 – Performance budget
• 00:48:27 – End user pain points
• 00:51:21 – Microservices and monoliths
• 00:54:01 – Performance questionnaire
• 01:00:31 – What is easier about performance planning upfront?
• 01:01:50 – Discoveries during the writing process
• 01:07:01 – Do LLMs write performant code?
• 01:12:37 – What are you excited about in the world of Python?
• 01:13:41 – What do you want to learn next?
• 01:15:02 – How can people follow your work online?
• 01:15:43 – Thanks and goodbye

Show Links:

• Fast by Default - Den Odell - Manning Discount Link
• Fast by Default
• Constraints and the Lost Art of Optimization - Den Odell
• Python 3.15 Preview: Sampling Profiler
• Tachyon: High frequency statistical sampling profile
• Profiling in Python: How to Find Performance Bottlenecks
• Episode #128: Using a Memory Profiler in Python & What It Can Teach You
• Episode #172: Measuring Multiple Facets of Python Performance With Scalene
• Running Python code in a sandbox with MicroPython and WASM
• Syntorial: The Ultimate Synthesizer Course
• Den Odell - LinkedIn
• Den Odell – Author. Staff Web Engineer.

Level up your Python skills with our expert-led courses:

• Speed Up Python With Concurrency
• Profiling Performance in Python
• Testing Your Code With Python's unittest

Support the podcast & join our community of Pythonistas

What are the key characteristics of complex systems, and what are practical patterns for tackling complex coding problems? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher covers an article about working on complex systems. The piece explores the distinction between complicated and complex problems. Each requires a fundamentally different approach to solve.

We dig into a couple of tutorials about creating repeatable data science pipelines. One covers validating DataFrames and tables with the pointblank library to build into a continuous integration workflow. The other explores how to use configuration files to build modular pipelines that avoid the pitfalls of hardcoded paths and parameters.

We also share other articles and projects from the Python community, including recent releases, three Python Enhancement Proposals (PEPs), the difficulties with breaking up lines of text, a static site generator for marimo notebooks, and a static analyzer for malicious Python code.

Course Spotlight: Sending Emails Using Python

Learn how to send emails with Python using SMTP and smtplib, attach files, format HTML messages, and personalize bulk emails.

Topics:

• 00:00:00 – Introduction
• 00:02:24 – Python 3.12.14, 3.11.16 and 3.10.21 Released
• 00:03:02 – Django Is Moving to an Annual Release Cycle
• 00:04:17 – PEP 828: Supporting ‘Yield From’ in Asynchronous Generators
• 00:04:56 – PEP 841: Adding Frozen Syntax to Optimize Immutable Types
• 00:07:04 – PEP 844: public and private Builtins
• 00:10:09 – Validating Data With Pointblank in Python
• 00:18:22 – Working on Complex Systems: Patterns I Learned at Google
• 00:22:14 – Video Course Spotlight
• 00:23:58 – Hydra for Python Configuration: Build Modular and Maintainable Pipelines
• 00:29:29 – Breaking Up (Lines) Is Hard to Do
• 00:35:00 – marimo-book: Static Site Generator for marimo Notebooks
• 00:38:06 – marimo-lsp: A language server and VS Code extension for marimo
• 00:38:31 – hexora: Static analysis of malicious Python code
• 00:40:01 – Thanks and goodbye

News:

• Python 3.12.14, 3.11.16 and 3.10.21 Released
• Django Is Moving to an Annual Release Cycle
• PEP 828: Supporting ‘Yield From’ in Asynchronous Generators (Accepted)
• PEP 841: Adding Frozen Syntax to Optimize Immutable Types (Draft)
• PEP 844: public and private Builtins (Draft)

Show Links:

• Validating Data With Pointblank in Python – Learn how to validate data in Python with Pointblank: declare quality checks, split clean from failing rows, and rerun validation plans from YAML.
• Working on Complex Systems: Patterns I Learned at Google – Explore the key characteristics of complex systems and learn practical patterns for tackling complex problems
• The Coder Cafe - Teiva Harsanyi – The Coder Cafe introduces vital software engineering concepts that will upgrade your day-to-day practice, regardless of your skill level. Discount Code (CODERCAFE40) Expires November 21st 2026.
• Hydra for Python Configuration: Build Modular and Maintainable Pipelines – Hardcoded paths and parameters can quickly drift out of sync across a data science project’s scripts. This article shows how to move them into Hydra configuration files, access values with dot notation, override settings from the command line, swap entire configuration groups, and run experiment sweeps with a single multi-run flag.
• Breaking Up (Lines) Is Hard to Do – Here’s a seemingly simple question: given a chunk of multi-line text, how do you split it and return an array? Unicode makes everything harder than it might first seem.

Projects:

• marimo-book: Static Site Generator for marimo Notebooks
• marimo-lsp: A language server and VS Code extension for marimo
• hexora: Static analysis of malicious Python code

Additional Links:

• pointblank - PyPI
• Episode #274: Preparing Data Science Projects for Production With Khuyen Tran
• marimo-book
• DartBrains - How can we understand how the brain works?
• marimo - Visual Studio Marketplace

Level up your Python skills with our expert-led courses:

• Getting Started With marimo Notebooks
• Managing Imports With Python's all
• Sending Emails Using Python

Support the podcast & join our community of Pythonistas

Why do AI systems silently fail? How can you set up a system that produces results while also reviewing and validating the work? This week on the show, Calvin Hendryx-Parker returns to discuss his recent talk “Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews.”

Calvin describes how silent failures can occur when handing a large document to an LLM for parsing. He describes it as the tragedy of context, where the LLM is confident, but you have no idea what it didn’t read. We discuss issues with file formats, dropped attachments, and silent truncation.

Calvin shares the example project from his talk, which includes Markdown files for the agents, hooks, skills, and scripts. He also discusses a variety of coding agents, skill frameworks, and CLI tools.

Course Spotlight: Coding With OpenCode: AI-Assisted Python

Learn how to use OpenCode for AI-assisted Python coding, using a free Gemini API key to analyze and refactor code right in your terminal.

Topics:

• 00:00:00 – Introduction
• 00:02:19 – Co-hosting Python Bytes
• 00:03:43 – Why AI Fails Silently talk
• 00:11:07 – Digging into silent failures and an audit trail
• 00:18:44 – How would you define hooks?
• 00:20:48 – Document extraction, embeddings, and Cowork
• 00:30:38 – Stripping the noise from file formats
• 00:34:43 – Things that are now baked into models
• 00:45:53 – Video Course Spotlight
• 00:47:23 – Leveraging agents to pick models
• 00:51:44 – GitHub repo for the project
• 00:54:57 – The current market around tokens
• 01:00:12 – What are you excited about in the world of Python?
• 01:03:02 – What do you want to learn next?
• 01:04:21 – The content the LLMs are trained on
• 01:07:36 – Upcoming events
• 01:08:38 – Thanks and goodbye

Show Links:

• Why AI Fails Silently and How to Fix It
• Claude Cowork - Claude by Anthropic
• List of All Oblique Strategies - Matt Rickard
• Pi Coding Agent
• goose - Your open source AI agent
• Codex CLI - ChatGPT Learn
• 2026 All Things Open AI: Orchestrate Agentic AI: Context, Checklists, and No-Miss Reviews - GitHub
• scaf: A template manager that simplifies bootstrapping and updating projects.
• Tau - Learn how coding agents are built.
• superpowers: An agentic skills framework & software development methodology that works.
• Hermes Agent — Open-Source AI Agent That Grows With You
• PyOhio 2026 - PyOhio 2026
• AWS re:Invent 2026 - Nov 30-Dec 4, Las Vegas
• Python Bytes - Python News Podcast
• beats the hell out of me - painfully - YouTube

Level up your Python skills with our expert-led courses:

• Getting Started With Claude Code
• Use Codex CLI to Enhance Your Python Projects
• Coding With OpenCode: AI-Assisted Python

Support the podcast & join our community of Pythonistas

What bottlenecks were preventing NumPy from scaling on free-threaded Python? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent article on the work done in NumPy to make multi-threaded workloads scale on the free-threaded build of CPython. It covers how removing the Global Interpreter Lock (GIL) is not enough on its own and how profiling revealed hidden bottlenecks in NumPy and CPython.

We also share other articles and projects from the Python community, including recent releases, a pair of announcements from PyPI, a tool for crawling your Django project, SIMD in pure Python, managing wildcard imports, a publication-quality Python Data Visualization library, and a tool to review your Django schema.

Spotlight: Modern Object-Oriented Python Book

Master classes, Python’s data model, and object-oriented design with Modern Object-Oriented Python. The best of Real Python’s OOP tutorials, curated into one book and fully up to date for modern Python.

Topics:

• 00:00:00 – Introduction
• 00:02:26 – Python 3.15.0 candidate 1 is here!
• 00:02:47 – Python 3.14.7 and 3.13.15 are now available!
• 00:03:00 – Django 6.1 released
• 00:03:39 – Planned Updates to the PyPI User Interface
• 00:04:13 – PyPI Releases Now Reject New Files After 14 Days
• 00:05:07 – PEP 837 – Extensible JSON serialization
• 00:06:20 – PEP 842: Module Exports
• 00:07:51 – Introducing django-crawl
• 00:12:02 – SIMD in Pure Python
• 00:18:29 – Managing Imports With Python’s __all__
• 00:29:34 – Spotlight: Modern Object-Oriented Python Book
• 00:30:53 – Scaling NumPy on Free-Threaded Python
• 00:37:53 – cnsplots: Python Data Visualization for Complex Datasets
• 00:42:19 – django-orm-lens: Django Schema Review
• 00:45:14 – Thanks and goodbye

News:

• Python 3.15.0 candidate 1 is here! - Python Insider
• Python 3.14.7 and 3.13.15 are now available! - Python Insider
• Django 6.1 released - Weblog - Django
• Planned Updates to the PyPI User Interface – Over the next few months a new user interface will be rolled out for the Python packaging website, PyPI. The rollout will be done in phases to make sure it is rock solid and to get community feedback. This post talks about the history of PyPI’s UI and what is changing.
• PyPI Releases Now Reject New Files After 14 Days – “The Python Package Index (PyPI) now rejects new files being uploaded to releases that are older than 14 days. This restriction was put in place to prevent old and long-stable releases from being poisoned”
• PEP 837 – Extensible JSON serialization
• PEP 842: Module Exports

Show Links:

• Introducing django-crawl – During a recent site migration, Adam used the Django test harness to crawl his site looking for missing security headers. In the process he uncovered seven other bugs for a project that had 100% code coverage. He has consolidated the crawling technique for testing into a library: django-crawl.
• SIMD in Pure Python – SIMD is Single Instruction, Multiple Data, an approach that does calculations with vectors of data sets. Python doesn’t support it natively, but libraries like NumPy allow you to code this way.
• Thoughts on “SIMD in Pure Python” - purplesyringa’s blog
• Managing Imports With Python’s __all__ – Learn how Python’s dunder all variable controls wildcard imports and shapes the public API your packages and modules expose.
• Scaling NumPy on Free-Threaded Python – A recap on the work done in NumPy and CPython to make multi-threaded NumPy workloads scale on the free-threaded build of CPython.

Projects:

• cnsplots: Python Data Visualization for Complex Datasets
• django-orm-lens: Django Schema Review

Additional Links:

• What’s up Python? __json__, __export__ and Astral stuff
• Episode #97: Improving Your Django and Python Developer Experience
• Python’s __all__: Packages, Modules, and Wildcard Imports – Tutorial
• Quiz: Managing Imports With Python’s __all__
• Smoke - The Amazing Hamster, Storing Food - YouTube
• Modern Object-Oriented Python Book – Real Python

Level up your Python skills with our expert-led courses:

• Python Modules and Packages: An Introduction
• NumPy Techniques and Practical Examples
• Managing Imports With Python's all

Support the podcast & join our community of Pythonistas

How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book “Building LLM Applications with DSPy.”

With large language models becoming embedded in production software, manual prompt engineering becomes brittle, time-consuming, and difficult to maintain. Brett shares how DSPy replaces manually writing and adjusting prompts with structured prompt programming.

We dig into how DSPy uses declarative signatures to define inputs and outputs. We also discuss how developers programmatically compile, tune, and optimize prompts for specific LLM-based tasks within an application.

Course Spotlight: Using LlamaIndex for RAG in Python

Learn how to set up LlamaIndex, load your data, build and persist an index, and run queries to get grounded answers with RAG in Python.

Topics:

• 00:00:00 – Introduction
• 00:01:50 – Working on a new book
• 00:02:53 – Writing with a partner
• 00:04:16 – Building LLM Applications with DSPy
• 00:05:22 – What is DSPy?
• 00:06:58 – Working with signatures and typing
• 00:09:53 – Prompt engineering failing to work for a new model
• 00:13:43 – What is an optimizer?
• 00:19:04 – Moving toward a new level of abstraction in prompting
• 00:23:19 – Prompts for the LLM within a consumer facing application
• 00:28:26 – Video Course Spotlight
• 00:30:21 – Example of using with RAG
• 00:38:45 – Example of a business plan tool
• 00:43:06 – The Bitter Lesson
• 00:46:03 – Structure of the book
• 00:49:55 – Advice on starting with DSPy
• 00:54:15 – Example of a transformed prompt
• 00:59:37 – What are you excited about in the world of Python?
• 01:01:47 – What do you want to learn next?
• 01:04:11 – What’s the best way to follow your work online?
• 01:04:40 – Thanks and goodbye

Show Links:

• Building LLM Applications with DSPy - Serj Smorodinsky and Brett Kennedy
• DSPy
• DSPy: The framework for programming—not prompting—language models - GitHub
• Tutorials Overview - DSPy
• GEPA optimization - DSPy
• Let the LLM Write the Prompts: An Intro to DSPy in Compound AI Pipelines - YouTube
• Episode #208: Detecting Outliers in Your Data With Python
• Tabular Models Benchmark: Performance Across 19 Datasets 2026
• Brett Kennedy on Manning
• Serj Smorodinsky on Manning
• W Brett Kennedy – Medium
• Brett Kennedy - LinkedIn

Level up your Python skills with our expert-led courses:

• Building Type-Safe LLM Agents With Pydantic AI
• Accessing Multiple AI Models With the OpenRouter API
• Using LlamaIndex for RAG in Python

Support the podcast & join our community of Pythonistas

Should you understand the entirety of your codebase? How familiar are you with Python’s built-in functions? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article by Sean Goedecke titled “In Defense of Not Understanding Your Codebase.” We dig into the differences in the scale of software projects and the factors that can obscure understanding of an entire codebase. We cover how opinions and practices people often argue for are based on the development practices from decades ago.

Christopher shares his recent video course that explores Python’s built-in functions. The course is divided into sections to help you find the right built-in for tasks involving math, data types, iterables, and I/O. We discuss which of these functions are frequently used in our code.

We also share other articles and projects from the Python community, including recent releases, querying with f-expressions in Django, replacing if-else chains, a Rust-based replacement for Python’s json module, and a couple of Python cheat sheet resources.

This episode is sponsored by HydraDB.

Course Spotlight: Exploring Python’s Built-in Functions

Learn Python’s built-in functions for math, data types, iterables, and I/O, and when to use each to write more Pythonic code.

Topics:

• 00:00:00 – Introduction
• 00:02:29 – Christopher’s Python News Song
• 00:03:18 – Python 3.15.0 Beta 4 Released
• 00:03:39 – PEP 838: Adding python-version to pyvenv.cfg
• 00:04:59 – PEP 840: Name Resolution in Class Namespaces
• 00:06:42 – Stop Using if-else Chains
• 00:13:46 – Sponsor: HydraDB
• 00:14:44 – Nifty Django Feature: F Expressions
• 00:20:23 – In Defense of Not Understanding Your Codebase
• 00:34:13 – Exploring Python’s Built-in Functions
• 00:45:26 – Video Course Spotlight
• 00:46:57 – Python strftime/strptime Directive Cheat Sheet
• 00:50:22 – Itertools Cheatsheet
• 00:52:59 – Introducing django-orjson
• 00:55:33 – Thanks and goodbye

News:

• Python 3.15.0 Beta 4 Released
• PEP 838: Adding python-version to pyvenv.cfg (Added)
• PEP 840: Name Resolution in Class Namespaces (Added)

Show Links:

• Stop Using if-else Chains – Learn a cleaner, more extensible way to dispatch logic in Python using dictionaries and function pointers instead of long if-else chains.
• Nifty Django Feature: F Expressions – Django’s F-Expression provides a way of querying fields from the ORM. They’re particularly handy to traverse relationships in more complex queries.
• F() Expressions - Django Documentation
• In Defense of Not Understanding Your Codebase – In this opinion piece, Sean argues that there is a difference in the thought process between maintaining smaller software projects vs larger ones, and that the former is over represented in engineering discussion in the internet.
• Exploring Python’s Built-in Functions – Learn Python’s built-in functions for math, data types, iterables, and I/O, and when to use each to write more Pythonic code.

Projects:

• Python strftime/strptime Directive Cheat Sheet
• Itertools Cheatsheet – Cheatsheet with visual diagrams that explain how the iterables from itertools work.
• Introducing django-orjson – orjson is a Rust-based replacement for Python’s json module. So what would Adam Johnson do with it? Make it easier to use in Django of course.
• orjson: Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
• django-orjson documentation

Additional Links:

• Primer on Python Decorators - Registering Plugins
• Python Built-in Functions: A Complete Guide
• Python Cheat Sheet – Real Python
• What’s in which Python - Ned Batchelder

Level up your Python skills with our expert-led courses:

• Using Python's datetime Module
• Python Basics: Conditional Logic and Control Flow
• Exploring Python's Built-in Functions

Support the podcast & join our community of Pythonistas

Which is more important, the model or the “harness” around an LLM? What are ways to assemble an efficient agentic developer workflow? This week on the show, Ayan Pahwa joins us to discuss harnessing, web scraping, and self-hosting Python applications.

Ayan is a developer advocate at Zyte and an experienced project builder. We discuss a recent article he wrote about creating an extension for the web scraping tool Scrapy. He also digs into his self-hosting setup for Python applications and tools.

Our discussion extends to the complexities of developing effective harnesses. Ayan shares his setup and how he navigated shifting from prompt engineering to context and loop engineering.

This episode is sponsored by HydraDB.

Course Spotlight: Introduction to Web Scraping With Python

In this video course, you’ll learn all about web scraping in Python. You’ll see how to parse data from websites and interact with HTML forms using tools such as Beautiful Soup and MechanicalSoup.

Topics:

• 00:00:00 – Introduction
• 00:02:01 – Scrapy and building an extension
• 00:08:46 – Zyte and the web scraping API
• 00:11:19 – Sponsor: HydraDB
• 00:12:22 – noalgotube project
• 00:15:49 – Homelab & self hosting projects
• 00:22:19 – What goes into a harness?
• 00:32:33 – Where did you start exploring LLM tools?
• 00:36:13 – Local models & edge computing
• 00:39:31 – ExtractPod and discussing Apple’s AI
• 00:43:35 – Video Course Spotlight
• 00:44:54 – Managing token use and tools
• 00:52:37 – What are you excited about in the world of Python?
• 00:55:10 – What do you want to learn next?
• 00:56:38 – What is the best way to follow your work online?
• 00:56:58 – Thanks and goodbye

Show Links:

• How to build your first Scrapy extension
• Web Scraping API - All-in-one Web Scraper - Zyte API
• Web Scraping With Scrapy and MongoDB – Real Python
• noalgotube: I Built My Own YouTube Feed Because the Algorithm Stopped Working for Me - CodeNSolder
• noalgotube: A personal content aggregator for YouTube channels and blog RSS feeds
• Why homelab? Building a proper self-hosted setup from scratch - CodeNSolder
• OPNsense: Open source, feature rich firewall and routing platform, offering cutting-edge network protection
• Proxmox - Powerful open-source server solutions
• Pi-hole – Network-wide Ad Blocking
• omni-tools: Self-hosted collection of powerful web-based tools for everyday tasks
• Frigate NVR
• Harness Engineering, part 1: What is an agent harness and why it matters
• My agentic coding setup: Claude Code, multi-agent orchestration, and how I actually work
• ExtractPod EP07 - AI Harnesses, our model usage and a Scottish dinner staple. - YouTube
• OpenCode - The open source AI coding agent
• OpenRouter
• Gemma 4 — Google DeepMind
• LM Studio Bionic - Agent for Open Models
• GLM-5.2: Built for Long-Horizon Tasks
• caveman: 🪨 why use many token when few token do trick — Claude Code skill
• ponytail: Makes your AI agent think like the laziest senior dev in the room
• Episode #301: Running Python Locally in a Sandbox
• Aillio – Bullet R2 - Coffee Roaster
• CodeNSolder
• Ayan Pahwa - Zyte
• HydraDB

Level up your Python skills with our expert-led courses:

• Introduction to Web Scraping With Python
• Getting Started With Claude Code
• Testing MCP Servers With a Python MCP Client

Support the podcast & join our community of Pythonistas

How many attempts have been made to remove Python’s Global Interpreter Lock (GIL)? How do they compare to the current approach? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent article about Thomas Wouters’ talk at PyCon US 2026. The talk, titled “Free-threaded Python: past, present, and future,” covers the efforts to remove the GIL starting in 1996. He explains that threads are complex, but they allow multiple tasks to run concurrently within a single process and its address space.

The GIL is how CPython implements threading. The GIL protects Python objects and their reference counts, which determine the current objects in use. The talk also looks forward and shares the current work to remove the GIL, now named free-threaded Python, and the goals for the near future.

We also share other articles and projects from the Python community, including community announcements, a roundup of recent Real Python tutorials and video courses, using uv in Production, employing Wagtail as Django admin on steroids, managing and measuring Python code quality, a pure-Python implementation of jq, and a project to bring interactivity to plotnine.

This episode is sponsored by AURI by Endor Labs

Course Spotlight: Thread Safety in Python: Locks and Other Techniques

In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe.

Topics:

• 00:00:00 – Introduction
• 00:03:08 – PEP 836: JIT Go Brrr: The Path to a Supported JIT Compiler for CPython
• 00:04:34 – PyCon US 2026 Videos Are Up
• 00:04:53 – Thinking About Running for the PSF Board? Let’s Talk!
• 00:05:30 – How to Get Started With the GitHub Copilot CLI
• 00:06:27 – Python 3.15 Preview: Upgraded JIT Compiler
• 00:07:23 – Testing MCP Servers With a Python MCP Client
• 00:08:10 – How to Use GitHub
• 00:08:50 – Why I Wrote PEP 832: Virtual Environment Discovery
• 00:09:46 – Free-Threaded Python: Past, Present, and Future
• 00:16:51 – Sponsor: AURI by EndorLabs
• 00:17:38 – uv in Production: The Speed Is Real, the Integration Isn’t Free
• 00:26:18 – Wagtail as Django Admin on Steroids
• 00:29:52 – Video Course Spotlight
• 00:31:11 – Managing and Measuring Python Code Quality
• 00:43:10 – purejq: A Pure-Python Implementation of jq
• 00:46:41 – ninejs: Bringing ✨interactivity✨ to plotnine
• 00:49:15 – Thanks and goodbye

News:

• PEP 836: JIT Go Brrr: The Path to a Supported JIT Compiler for CPython (Draft)
• The Path to a Supported JIT Compiler for CPython
• PyCon US 2026 Videos Are Up
• Thinking About Running for the PSF Board? Let’s Talk! – The Python Software Foundation Board has announced two office-hour sessions dedicated to giving information on running for the PSF Board. If you’re thinking of running in the upcoming election, these sessions can help you understand the ins and outs.

Real Python News:

• How to Get Started With the GitHub Copilot CLI – Tutorial
• Managing and Measuring Python Code Quality – Video CoursePython 3.15 Preview: Upgraded JIT Compiler – Tutorial
• Testing MCP Servers With a Python MCP Client – Video Course
• How to Use GitHub – Real Python

Show Links:

• Why I Wrote PEP 832: Virtual Environment Discovery – PEP 832 proposes a way to describe where your virtual environment is so that your tools can look in the right place. Although a relatively simple proposal it has caused some contention in the community. This post by Brett, the PEP’s author, describes his reasoning.
• Free-Threaded Python: Past, Present, and Future – This post summarizes a talk by core developer Thomas Wouters at PyCon US 2026 on Free-threaded Python: the attempt to remove the GIL. It describes why it is being done and what future work looks like.
• Free threaded Python past, present and future - YouTube
• uv in Production: The Speed Is Real, the Integration Isn’t Free – Oleg’s work moved their tooling from pip to uv and lived with it for ~90 days. They discovered that the speed is real, but that doesn’t mean there aren’t complications.
• Wagtail as Django Admin on Steroids – Wagtail can do pretty much everything the Django Admin can do, but includes a much more modern UI and more features. This article shows you how to use Wagtail as an Admin alternative.
• Managing and Measuring Python Code Quality – Master Python code quality tools like linters, formatters, type checkers, and profilers to measure, manage, and improve the code you write.
• Python Code Quality: Best Practices and Tools – Real Python

Projects:

• purejq: A Pure-Python Implementation of jq
• ninejs: Bringing ✨interactivity✨ to plotnine
• ninejs - Documentation

Additional Links:

• Episode #297: Improving Python Through PEPs and Protocols
• PEP 766
• PEP 8 – Style Guide for Python Code | peps.python.org
• How to Write Beautiful Python Code With PEP 8
• AURI for Developers - AI-Native AppSec Platform - Endor Labs

Level up your Python skills with our expert-led courses:

• Threading in Python
• Thread Safety in Python: Locks and Other Techniques
• Testing MCP Servers With a Python MCP Client

Support the podcast & join our community of Pythonistas

How can you improve your LLM agent systems through specification enrichment? What are the advantages of having an LLM act as a judge within an agent system? This week on the show, Senior IEEE Member and Quality Engineer Suneet Malhotra joins us to discuss building and evaluating agentic architecture.

Suneet Malhotra is an independent practitioner-researcher with 18 years of experience in Quality Engineering (QE) and test automation for consumer-scale platforms. He discusses building specification-enrichment loops, monitoring performance, and using Cohen’s kappa to measure agreement between LLM judgments.

Suneet is currently publishing multiple papers that are under peer review on these topics. He also provides links to his work and GitHub projects if you want to experiment with these concepts and methods yourself.

Quick Survey: Get more out of the podcast show notes

Video Course Spotlight: Testing MCP Servers With a Python MCP Client

Learn how to build a Python MCP client that tests MCP servers from your terminal. List their tools, prompts, and resources, then call each one.

Topics:

• 00:00:00 – Introduction
• 00:00:56 – Survey: RP Podcast show notes
• 00:02:11 – How did you get into testing?
• 00:05:04 – Has working for large public-facing corporations changed how you approach testing?
• 00:07:06 – Writing a paper on LLM-as-Judge
• 00:09:22 – Looking across the Software Development Lifecycle
• 00:14:46 – Agentic AI: theater vs methodology
• 00:17:23 – Specification enrichment
• 00:27:52 – Video Course Spotlight
• 00:29:18 – Saving the specifications
• 00:31:27 – Using the LLM as a judge & Cohen’s kappa
• 00:39:31 – How can people try out the project?
• 00:43:35 – What are some of the failure modes you’ve seen?
• 00:50:26 – What’s an inexpensive way to try these ideas out?
• 00:54:04 – What are you excited about in the world of Python?
• 00:56:14 – What do you want to learn next?
• 00:57:14 – How can people follow your work online?
• 00:57:32 – Thanks and goodbye

Show Links:

• Suneet Malhotra - AI-Driven Quality Engineering Leader
• SuneetMalhotra - GitHub
• Suneet Malhotra - ORCID
• Cross-Layer Observability for LLM-Assisted Test Automation — Reference Implementation and Evaluation Data - Zenodo
• Specification Enrichment v28 (EISEJ submission) — Reference implementation and empirical evaluation - Zenodo
• Visual Oracle Bench — Two-Judge Synthetic-HTML Pilot for LLM-as-Judge Visual Regression Detection with Specificity Reporting - Zenodo
• Cohen’s kappa - Wikipedia
• Suneet Malhotra - LinkedIn
• Survey: Get more out of every Real Python Podcast episode

Level up your Python skills with our expert-led courses:

• Improving Your Tests With the Python Mock Object Library
• Building Type-Safe LLM Agents With Pydantic AI
• Testing MCP Servers With a Python MCP Client

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How do you avoid the risk of running a Python application locally that could be malicious, break your code, or leak private data? How can you create a sandboxed local environment using WASM and MicroPython? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We cover a recent article by previous guest Simon Willison titled “Running Python code in a sandbox with MicroPython and WASM.” Simon has been experimenting for years on how to run Python code in a sandbox to reduce the risk of trying out new software, untrusted libraries, and wild ideas. He’s developed a solution using WASM and MicroPython and is sharing it as an alpha package on PyPI.

We also share other articles and projects from the Python community, including new releases, community announcements, a roundup of recent Real Python tutorials and video courses, a plugin case study using Pluggy, a look at whether you’re expected to run five type-checkers now, wrapping programs using the subprocess module, a project for star charts and maps, and a tool for trend detection in Python.

This episode is sponsored by DataDriven.

Spotlight: Codex for Python Developers: Hands-On Agentic Coding Course

Most Python developers use AI as fancy autocomplete. This 2-day live course teaches you to build entire projects with Codex, an AI agent that works inside your codebase.

Topics:

• 00:00:00 – Introduction
• 00:02:48 – PSF Board Election Dates for 2026
• 00:03:28 – Python 3.15.0 beta 3 is here!
• 00:03:58 – PEP 835: Shorthand Syntax for Annotated Type Metadata
• 00:05:13 – Announcing the Search for a DSF Executive Director
• 00:05:53 – Django 6.1 beta 1 released
• 00:06:24 – PyData London 26 Videos Released
• 00:06:50 – Implementing Interfaces in Python: ABCs and Protocols
• 00:07:36 – Building Python Skills for the Job Market
• 00:08:21 – Context Engineering for Python Codebases
• 00:09:14 – Python for Data Analysis: A Practical Guide
• 00:09:47 – Using LlamaIndex for RAG in Python
• 00:10:07 – Django Tasks: Exploring the Built-in Tasks Framework
• 00:10:59 – Plugins Case Study: Pluggy
• 00:15:10 – Sponsor: DataDriven
• 00:15:57 – Pyodide 314.0 Release
• 00:18:54 – Python in a Sandbox With MicroPython and WASM
• 00:22:35 – The subprocess Module: Wrapping Programs With Python
• 00:30:08 – Spotlight: Codex for Python Developers
• 00:31:51 – Are You Expected to Run 5 Type-Checkers Now?
• 00:39:41 – starplot: ✨ Star charts and maps in Python
• 00:42:01 – marimo-tutorials: Collection of Marimo Tutorials
• 00:42:48 – pytrendy: Trend Detection in Python
• 00:44:35 – Thanks and goodbye

News:

• PSF Board Election Dates for 2026
• Python 3.15.0 beta 3 is here! - Python Insider
• PEP 835: Shorthand Syntax for Annotated Type Metadata (Added)
• Announcing the Search for a DSF Executive Director
• Django 6.1 beta 1 released - Django Weblog
• PyData London 26 Videos Released

Real Python News:

• Implementing Interfaces in Python: ABCs and Protocols
• Building Python Skills for the Job Market
• Context Engineering for Python Codebases
• Python for Data Analysis: A Practical Guide
• Using LlamaIndex for RAG in Python
• Django Tasks: Exploring the Built-in Tasks Framework

Topics:

• Plugins Case Study: Pluggy – Pluggy is an open source plugin system used by frameworks such as pytest and tox. This article introduces you to how it works and what you can do with it.
• Pyodide 314.0 Release – This post announces the Pyodide 314.0 release and describes its features, including a focus on standardization and packaging. You can now build Pyodide wheels and post them to PyPI.
• Python in a Sandbox With MicroPython and WASM – Simon’s been in search of the perfect code sandbox. This article is about his latest attempt and covers why he wants a sandbox and what tech he’s used to achieve it.
• The subprocess Module: Wrapping Programs With Python – Python’s subprocess module allows you to run shell commands and manage external processes directly from your Python code. By using subprocess, you can execute shell commands like ls or dir, launch applications, and handle both input and output streams.
• Are You Expected to Run 5 Type-Checkers Now? – Library maintainers may feel overwhelmed by the plurality of type checkers that exist. We offer some guidance on how to focus their efforts where they matter most.

Projects:

• starplot: ✨ Star charts and maps in Python
• marimo-tutorials: Collection of Marimo Tutorials
• pytrendy: Trend Detection in Python

Additional Links:

• Episode #226: PySheets: Spreadsheets in the Browser Using PyScript
• Datasette: An open source multi-tool for exploring and publishing data
• Exploring Astrophysics in Python With pandas and Matplotlib
• Using Astropy for Astronomy With Python
• Investigating Quasar Data With Polars and Interactive marimo Notebooks
• JupyterLite — JupyterLite 0.8.0 documentation
• DataDriven - Data Engineer Interview Practice Problems

Level up your Python skills with our expert-led courses:

• Using Astropy for Astronomy With Python
• Using LlamaIndex for RAG in Python
• Building Python Skills for the Job Market

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Do you feel like your Python skills are atrophying after using LLM coding tools? How do you add the right kind of friction into your coding routine to keep your developer instincts sharp? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article by previous guest Bob Belderbos about developers keeping their instincts when AI is writing the code. He stresses the importance of the right kind of friction for maintaining skills and scheduling a deliberate coding practice routine.

We also share other articles and projects from the Python community, including new releases, a roundup of recent Real Python tutorials and video courses, sending emails with Python, libraries to enhance your Python Polars workflows, exploring Django Integrity-Policy, a next-generation HTTP client for Python, and a project to detect lazy imports incompatibilities.

This episode is sponsored by AURI by Endor Labs

Course Spotlight: Accessing Multiple AI Models With the OpenRouter API

Access models from popular AI providers in Python through OpenRouter’s unified API with smart routing, fallbacks, and cost controls.

Topics:

• 00:00:00 – Introduction
• 00:02:53 – Python 3.14.6, 3.13.14, and 3.15.0b2 Released
• 00:03:20 – Django Security Releases Issued: 6.0.6 and 5.2.15
• 00:03:35 – PyPy v7.3.23 Released
• 00:04:25 – Python sleep(): How to Add Time Delays to Your Code
• 00:04:59 – Structuring Your Python Script
• 00:05:36 – How to Use GitHub Copilot Code Review in Pull Requests
• 00:06:13 – Accessing Multiple AI Models With the OpenRouter API
• 00:07:25 – Cursor vs Windsurf: Which AI Code Editor Is Best for Python?
• 00:08:28 – Sending Emails With Python
• 00:16:31 – Sponsor:AURI from Endor Labs
• 00:17:17 – Announcing Polars 1.41
• 00:19:43 – Libraries for Your Python Polars Workflows
• 00:23:42 – Django: Introducing Django-Integrity-Policy
• 00:28:39 – Video Course Spotlight
• 00:30:21 – Keep Your Developer Instincts When AI Writes the Code
• 00:39:58 – Lifeguard: Detect Lazy Imports Incompatibilities
• 00:42:46 – httpx2: A Next Generation HTTP Client for Python
• 00:45:34 – Thanks and goodbye

News:

• Python 3.14.6 and 3.13.14 Released
• Python 3.15.0b2 Released
• Django Security Releases Issued: 6.0.6 and 5.2.15
• PyPy v7.3.23 Released

Real Python News:

• Python sleep(): How to Add Time Delays to Your Code - Tutorial
• Structuring Your Python Script - Video Course
• How to Use GitHub Copilot Code Review in Pull Requests – Tutorial
• Accessing Multiple AI Models With the OpenRouter API – Video Course
• Cursor vs Windsurf: Which AI Code Editor Is Best for Python? – Tutorial

Show Links:

• Sending Emails With Python – Learn how to send emails with Python using SMTP, attach files, format HTML messages, and personalize bulk emails for your contact list.
• Announcing Polars 1.41 – Polars 1.41 is out and this post covers the new features it includes. Learn about faster parquet metadata decoding, nested subplan elimination, and more.
• Libraries for Your Python Polars Workflows – Four excellent libraries for your data science workflow with support for Polars DataFrames
• Django: Introducing Django-Integrity-Policy – Recently, browsers have added support for the new Integrity-Policy response header (Firefox 145+, Chrome 138+). Adam quickly went to work to build a library that enables your Django project to take advantage of the feature.
• How to Keep Your Developer Instincts When AI Writes the Code – The promise was less friction. The cost, it turns out, is instinct, a high price to pay. Bob’s answer: add deliberate practice to your routine, and keep the struggle.

Projects:

• Lifeguard: Detect Lazy Imports Incompatibilities
• httpx2: A Next Generation HTTP Client for Python

Additional Links:

• OpenRouter
• Episode #214: Build Captivating Display Tables in Python With Great Tables
• Using ggplot in Python: Visualizing Data With plotnine – Tutorial
• Learning Rust Made Me a Better Python Developer
• HTTPXYZ
• AURI for Developers - AI-Native AppSec Platform - Endor Labs

Level up your Python skills with our expert-led courses:

• Graph Your Data With Python and ggplot
• Structuring Your Python Script
• Accessing Multiple AI Models With the OpenRouter API

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What’s happening at EuroPython 2026? The conference celebrates its 25th anniversary this year in Kraków, Poland. This week on the show, organizers Mia Bajić and Daria Linhart Grudzien join me to discuss this year’s conference.

Mia serves as the Vice Chair of the EuroPython Society, and Daria leads the EuroPython communications team. We dig into the details of the conference, including the wide variety of tracks, the reasoning for selecting the location, and the additional activities surrounding the event.

We talk about volunteering, organizing, and continuing support for conferences. Mia and Daria also share the talks they’re excited to check out and how they use Python currently.

Course Spotlight: Building Command Line Interfaces With argparse

In this step-by-step Python video course, you’ll learn how to take your command line Python scripts to the next level by adding a convenient command line interface that you can write with argparse.

Topics:

• 00:00:00 – Introduction
• 00:02:27 – EuroPython 2026 details
• 00:05:02 – Daria and Mia’s roles with the conference
• 00:09:08 – Practices to continue growing a conference
• 00:13:16 – What makes EuroPython different?
• 00:18:40 – Video Course Spotlight
• 00:20:17 – Wide variety of tracks, talks, and topics
• 00:28:54 – How are you using Python currently?
• 00:32:38 – What are you excited about in the world of Python?
• 00:35:12 – What do you want to learn next?
• 00:37:30 – How can people follow the work you do online?
• 00:39:06 – Thanks and goodbye

Show Links:

• EuroPython 2026 - July 13-19, 2026 - Kraków, Poland
• 🎂 25th Anniversary of EuroPython: Social Media Challenge 🎂
• Schedule for 2026
• ICE Kraków Congress Centre
• EuroPython: Overview - LinkedIn
• EuroPython Conference - YouTube
• EuroPython - Fosstodon
• EuroPython Conference (@europython) - Instagram
• EuroPython (@europython) - TikTok
• EuroPython 2026, Kraków (@europython.eu) - Bluesky
• EuroPython (@europython) - X

Mia Links:

• Mia Bajić - LinkedIn
• Mia Bajić (@clytaemnestra_) - Instagram
• Mia Bajić (@clytaemnestra.bsky.social) - Bluesky
• Behind the Commit - YouTube
• mia@europython.eu

Daria Links:

• Daria Linhart Grudzien - LinkedIn
• daria@europython.eu

Level up your Python skills with our expert-led courses:

• Building Command Line Interfaces With argparse
• Structuring Your Python Script
• Accessing Multiple AI Models With the OpenRouter API

Support the podcast & join our community of Pythonistas

How can you easily reduce the size of a Python Docker container? What are the exceptions you should catch in your code? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.

We cover a tutorial by Khuyen Tran at CodeCut about shrinking the size of a Python Docker container. The piece explores SlimToolKit, which analyzes a container at runtime, identifies what files are used, and then builds a minimal image with only those dependencies.

We dig into a recent piece by Trey Hunner about Python exceptions. When trying to determine a strategy to handle potential errors, which exceptions should you catch and which should you leave unhandled?

We also share other articles and projects from the Python community, including recent releases, two PEPs that have been deferred to 3.16, a critical vulnerability in an open-source ASGI framework, resolving a lazy import manually, a project to anonymize sensitive PII data, and a tool for loading Django settings from a TOML file.

This episode is sponsored by AURI by Endor Labs.

Course Spotlight: Raising and Handling Python Exceptions

In this course, you’ll learn what an exception is and how it differs from a syntax error. You’ll learn about raising exceptions, making assertions, and catching exceptions to change the control flow of your program using the try, except, else, and finally keywords.

Topics:

• 00:00:00 – Introduction
• 00:02:32 – Django 6.1 Alpha 1 Released
• 00:03:07 – Nuitka Python Compiler Release 4.1
• 00:04:00 – PEP 813: The Pretty Print Protocol (Deferred to 3.16)
• 00:04:28 – PEP 830: Add Timestamps to Exceptions and Tracebacks
• 00:04:50 – Millions of AI agents imperiled by critical vulnerability in open source package
• 00:07:27 – What Types of Exceptions Should You Catch?
• 00:13:28 – Sponsor: AURI from Endor Labs
• 00:14:18 – PyCon US 2026 Packaging Summit Recap
• 00:18:39 – Slim Down Python Docker Containers
• 00:24:17 – Video Course Spotlight
• 00:25:45 – Resolve a Lazy Import Manually
• 00:28:04 – presidio: Detect, Redact, & Anonymize Sensitive Data (PII)
• 00:32:37 – dj-toml-settings: Load Django settings from a TOML file
• 00:37:14 – Thanks and goodbye

News:

• Django 6.1 Alpha 1 Released
• Nuitka Python Compiler Release 4.1
• PEP 813: The Pretty Print Protocol (Deferred to 3.16)
• PEP 830: Add Timestamps to Exceptions and Tracebacks (Deferred to 3.16)
• Millions of AI agents imperiled by critical vulnerability in open source package - Ars Technica
• Missing Host header validation poisons request.url.path, bypassing path-based security checks · Advisory · Kludex/starlette

Show Links:

• What Types of Exceptions Should You Catch? – The trickiest programming bugs are often caused by catching exceptions that you didn’t mean to catch or handling exceptions in ways that obfuscate the actual error that’s occurring. Which exceptions should you catch and which should you leave unhandled?
• PyCon US 2026 Packaging Summit Recap – Per-talk notes from the PyCon US 2026 Packaging Summit, including: Emma Smith on Wheel 2.0 and Zstandard compression, Mike Fiedler on PyPI abuse vectors, Mahe Iram Khan on ecosystems, lightning talks on PEP 772, mobile wheels, AI accelerator variants, and the roundtable discussions.
• Slim Down Python Docker Containers – Learn how SlimToolKit can reduce a Python Docker image by analyzing what your app actually uses at runtime. This tutorial walks through slimming a Chainlit LLM chatbot image, shows where container bloat comes from, and explains how to avoid breaking lazily loaded Python frameworks.
• Resolve a Lazy Import Manually – Learn how to work around the Python 3.15 machinery to resolve an explicit lazy import manually.
• TIL #141 – Inspect a lazy import - mathspp

Projects:

• presidio: Detect, Redact, & Anonymize Sensitive Data (PII)
• dj-toml-settings: Load Django settings from a TOML file

Additional Links:

• Using raise for Effective Exceptions - Real Python Video Course
• Working With Python’s Built-in Exceptions – Real Python Video Course
• Episode #177: Welcoming PyPI’s Safety & Security Engineer Mike Fiedler
• Chainlit - Build AI applications
• AURI for Developers - AI-Native AppSec Platform - Endor Labs

Level up your Python skills with our expert-led courses:

• Raising and Handling Python Exceptions
• Advanced Python import Techniques
• Working With Python's Built-in Exceptions

Support the podcast & join our community of Pythonistas